- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) - Xi'an, China (2019.6.19-2019.6.21)] 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) - Photovoltaic consumption in distribution network considering shiftable load
摘要： In this paper, a joint feature selection and parameter estimation algorithm is presented for hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs). New parameters, feature saliencies, are introduced to the model and used to select features that distinguish between states. The feature saliencies represent the probability that a feature is relevant by distinguishing between state-dependent and state-independent distributions. An expectation maximization algorithm is used to calculate maximum a posteriori estimates for model parameters. An exponential prior on the feature saliencies is compared with a beta prior. These priors can be used to include cost in the model estimation and feature selection process. This algorithm is tested against maximum likelihood estimates and a variational Bayesian method. For the HMM, four formulations are compared on a synthetic data set generated by models with known parameters, a tool wear data set, and data collected during a painting process. For the HSMM, two formulations, maximum likelihood and maximum a posteriori, are tested on the latter two data sets, demonstrating that the feature saliency method of feature selection can be extended to semi-Markov processes. The literature on feature selection speci?cally for HMMs is sparse, and non-existent for HSMMs. This paper ?lls a gap in the literature concerning simultaneous feature selection and parameter estimation for HMMs using the EM algorithm, and introduces the notion of selecting features with respect to cost for HMMs.
关键词： maximum a posteriori estimation.,hidden Markov models,hidden semi-Markov models,Feature selection
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Performance Loss Rate Consistency and Uncertainty Across Multiple Methods and Filtering Criteria
摘要： In this paper, a joint feature selection and parameter estimation algorithm is presented for hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs). New parameters, feature saliencies, are introduced to the model and used to select features that distinguish between states. The feature saliencies represent the probability that a feature is relevant by distinguishing between state-dependent and state-independent distributions. An expectation maximization algorithm is used to calculate maximum a posteriori estimates for model parameters. An exponential prior on the feature saliencies is compared with a beta prior. These priors can be used to include cost in the model estimation and feature selection process. This algorithm is tested against maximum likelihood estimates and a variational Bayesian method. For the HMM, four formulations are compared on a synthetic data set generated by models with known parameters, a tool wear data set, and data collected during a painting process. For the HSMM, two formulations, maximum likelihood and maximum a posteriori, are tested on the latter two data sets, demonstrating that the feature saliency method of feature selection can be extended to semi-Markov processes. The literature on feature selection speci?cally for HMMs is sparse, and non-existent for HSMMs. This paper ?lls a gap in the literature concerning simultaneous feature selection and parameter estimation for HMMs using the EM algorithm, and introduces the notion of selecting features with respect to cost for HMMs.
关键词： hidden Markov models,maximum a posteriori estimation,hidden semi-Markov models,Feature selection
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP) - Richland, WA, USA (2019.10.20-2019.10.23)] 2019 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP) - The Anti-Interference Method of Michelson Optical Fiber Interferometer for GIS Partial Discharge Ultrasonic Detection
摘要： Performance of automatic speech recognition (ASR) systems can significantly be improved by integrating further sources of information such as additional modalities, or acoustic channels, or acoustic models. Given the arising problem of information fusion, striking parallels to problems in digital communications are exhibited, where the discovery of the turbo codes by Berrou et al. was a groundbreaking innovation. In this paper, we show ways how to successfully apply the turbo principle to the domain of ASR and thereby provide solutions to the above-mentioned information fusion problem. The contribution of our work is fourfold: First, we review the turbo decoding forward-backward algorithm (FBA), giving detailed insights into turbo ASR, and providing a new interpretation and formulation of the so-called extrinsic information being passed between the recognizers. Second, we present a real-time capable turbo-decoding Viterbi algorithm suitable for practical information fusion and recognition tasks. Then we present simulation results for a multimodal example of information fusion. Finally, we prove the suitability of both our turbo FBA and turbo Viterbi algorithm also for a single-channel multimodel recognition task obtained by using two acoustic feature extraction methods. On a small vocabulary task (challenging, since spelling is included), our proposed turbo ASR approach outperforms even the best reference system on average over all SNR conditions and investigated noise types by a relative word error rate (WER) reduction of 22.4% (audio-visual task) and 18.2% (audio-only task), respectively.
关键词： hidden Markov models,Speech recognition,multimedia systems,robustness,iterative decoding
更新于2025-09-19 17:13:59
-
A Fusion Firefly Algorithm with Simplified Propagation for Photovoltaic MPPT under Partial Shading Conditions
摘要： Performance of automatic speech recognition (ASR) systems can significantly be improved by integrating further sources of information such as additional modalities, or acoustic channels, or acoustic models. Given the arising problem of information fusion, striking parallels to problems in digital communications are exhibited, where the discovery of the turbo codes by Berrou et al. was a groundbreaking innovation. In this paper, we show ways how to successfully apply the turbo principle to the domain of ASR and thereby provide solutions to the above-mentioned information fusion problem. The contribution of our work is fourfold: First, we review the turbo decoding forward-backward algorithm (FBA), giving detailed insights into turbo ASR, and providing a new interpretation and formulation of the so-called extrinsic information being passed between the recognizers. Second, we present a real-time capable turbo-decoding Viterbi algorithm suitable for practical information fusion and recognition tasks. Then we present simulation results for a multimodal example of information fusion. Finally, we prove the suitability of both our turbo FBA and turbo Viterbi algorithm also for a single-channel multimodel recognition task obtained by using two acoustic feature extraction methods. On a small vocabulary task (challenging, since spelling is included), our proposed turbo ASR approach outperforms even the best reference system on average over all SNR conditions and investigated noise types by a relative word error rate (WER) reduction of 22.4% (audio-visual task) and 18.2% (audio-only task), respectively.
关键词： robustness,Speech recognition,multimedia systems,iterative decoding,hidden Markov models
更新于2025-09-16 10:30:52
-
[IEEE 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) - JeJu, Korea (South) (2018.6.24-2018.6.26)] 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) - Image Pattern Classification Using MFCC and HMM
摘要： We propose a novel method for recognizing temporally or spatially varying patterns using MFCC (mel-frequency ceptral coefficient) and HMM (hidden Markov model). MFCC and HMM have been adopted as de facto standard for speech recognition. It is very useful time-domain signals with temporally varying characteristics. Most images have characteristical patterns, so HMM is expected to model them very efficiently. We suggest efficient pattern classification algorithm with MFCC and HMM, and showed its improved performance in MNIST and fashion MNIST databases.
关键词： Hidden Markov Models (HMMs),Discrete Cosine Transform (DCT),Mixed National Institute of Standards and Technology (MNIST),Mel-Frequency Cepstral Coefficients (MFCC)
更新于2025-09-09 09:28:46